Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 2,989 Bytes
76513ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | #pragma once
#include "common.h"
#include "arg.h"
#include <string>
#include <vector>
#include <map>
#include <set>
//
// INI preset parser and writer
//
constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default";
struct common_preset_context;
struct common_preset {
std::string name;
// options are stored as common_arg to string mapping, representing CLI arg and its value
std::map<common_arg, std::string> options;
// convert preset to CLI argument list
std::vector<std::string> to_args(const std::string & bin_path = "") const;
// convert preset to INI format string
std::string to_ini() const;
// TODO: maybe implement to_env() if needed
// modify preset options where argument is identified by its env variable
void set_option(const common_preset_context & ctx, const std::string & env, const std::string & value);
// unset option by its env variable
void unset_option(const std::string & env);
// get option value by its env variable, return false if not found
bool get_option(const std::string & env, std::string & value) const;
// merge another preset into this one, overwriting existing options
void merge(const common_preset & other);
// apply preset options to common_params
// optionally specify handled_keys to only apply a subset of options (identified by their env), if empty, apply all options
void apply_to_params(common_params & params, const std::set<std::string> & handled_keys = std::set<std::string>()) const;
};
// interface for multiple presets in one file
using common_presets = std::map<std::string, common_preset>;
// context for loading and editing presets
struct common_preset_context {
common_params default_params; // unused for now
common_params_context ctx_params;
std::map<std::string, common_arg> key_to_opt;
bool filter_allowed_keys = false;
std::set<std::string> allowed_keys;
// if only_remote_allowed is true, only accept whitelisted keys
common_preset_context(llama_example ex);
// load presets from INI file
common_presets load_from_ini(const std::string & path, common_preset & global) const;
// generate presets from cached models
common_presets load_from_cache() const;
// generate presets from local models directory
// for the directory structure, see "Using multiple models" in server/README.md
common_presets load_from_models_dir(const std::string & models_dir) const;
// generate one preset from CLI arguments
common_preset load_from_args(int argc, char ** argv) const;
// cascade multiple presets if exist on both: base < added
// if preset does not exist in base, it will be added without modification
common_presets cascade(const common_presets & base, const common_presets & added) const;
// apply presets over a base preset (same idea as CSS cascading)
common_presets cascade(const common_preset & base, const common_presets & presets) const;
};
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